U.S. patent number 10,980,433 [Application Number 16/362,527] was granted by the patent office on 2021-04-20 for health monitoring and guidance.
This patent grant is currently assigned to LIVMOR, INC.. The grantee listed for this patent is Livmor, Inc.. Invention is credited to Ross Grady Baker, Jr., Ken Persen.
United States Patent |
10,980,433 |
Persen , et al. |
April 20, 2021 |
Health monitoring and guidance
Abstract
A photoplethysmographic (PPG) signal communicated by a PPG
sensor of a wearable device worn by a user may be received by a
processor. The processor may detect a plurality of heartbeats of
the user from the PPG-signal, determine a heart rate of the user
based on at least the plurality of heartbeats, determine a heart
rate variability (HRV) based on the plurality of heartbeats,
determine a respiration rate of the user based on a low frequency
component of the PPG signal, and determine whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate. The processor may cause the display of
information related to the stress state of the user, and
instructions and/or advice for reducing a stress level of the
user.
Inventors: |
Persen; Ken (Dove Canyon,
CA), Baker, Jr.; Ross Grady (Bellaire, TX) |
Applicant: |
Name |
City |
State |
Country |
Type |
Livmor, Inc. |
Irvine |
CA |
US |
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Assignee: |
LIVMOR, INC. (Irvine,
CA)
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Family
ID: |
1000005497559 |
Appl.
No.: |
16/362,527 |
Filed: |
March 22, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20190290147 A1 |
Sep 26, 2019 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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16035568 |
Jul 13, 2018 |
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62535391 |
Jul 21, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
5/02416 (20130101); A61B 5/681 (20130101); A61B
5/02405 (20130101); A61B 5/0006 (20130101); A61B
5/1118 (20130101); A61B 5/14532 (20130101); A61B
5/0205 (20130101) |
Current International
Class: |
A61B
5/024 (20060101); A61B 5/11 (20060101); A61B
5/00 (20060101); A61B 5/145 (20060101); A61B
5/0205 (20060101) |
Field of
Search: |
;361/679.01-679.03
;368/10-14 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Kikillus, N., et al., Three Different Algorithms for Identifying
Patients Suffering from Atrial Fibrillation during Atrial
Fibrillation Free Phases of the ECG; Computers in Cardiology 2007,
vol. 34, pp. 801-804. cited by applicant .
International Search Report and Written Opinion dated Sep. 4, 2019,
for related international application No. PCT/US2019/024668. cited
by applicant .
Meredith, D. J., et al.. Photoplethysmographic derivation of
respiratory rate: a review of relevant physiology; Journal of
Medical Engineering & Technology. 2012. pp. 1-8. cited by
applicant .
Pirhonen, Mikko, et al.. Acquiring Respiration Rate from
Photoplethysmographic Signal by Recursive Bayesian Tracking of
Intrinsic Modes in Time-Frequency Spectra; BioMediTech Institute
and Faculty of Biomedical Sciences and Engineering, Tampere
University of Technology. May 24, 2018. pp. 1-16. cited by
applicant .
Park, Chanki and Boreom Lee. Real-time estimation of respiratory
rate from a photplethysmogram using an adaptive lattice notch
filter. BioMedical Engineering OnLine. 2014. 13:170. pp. 1-18.
cited by applicant .
Sioni, Riccardo and Luca Chittaro. Stress Detection Using
Physiological Sensors. Physiological Computing. Oct. 2015. pp.
26-33. cited by applicant .
Roscoe, A.H.. Assessing pilot workload. Why measure heart rate, HRV
and respiration? Biological Psychology. 34. (1192) pp. 259-287.
cited by applicant .
International Search Report and Written Opinion dated Jun. 5, 2019,
for corresponding international application No. PCT/US2019/023717.
cited by applicant .
International Preliminary Report on Patentability and Written
Opinion dated Oct. 8, 2020, for related International Application
No. PCT/US2019/024668. cited by applicant.
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Primary Examiner: Kuo; Jonathan T
Attorney, Agent or Firm: Pillsbury Winthrop Shaw Pittman
LLP
Parent Case Text
RELATED APPLICATION(S)
This application claims priority to and the benefit of U.S. patent
application Ser. No. 16/035,568, filed Jul. 13, 2018, titled
"Systems and Methods for Health Monitoring and Guidance," which
claims priority to U.S. Provisional Patent Application No.
62/535,391, filed Jul. 21, 2017, both of which are hereby
incorporated by reference in their entirety.
Claims
What is claimed is:
1. A computer program product comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by at least one programmable processor, cause the at least one
programmable processor to perform operations comprising: receiving,
at the at least one programmable processor, a photoplethysmographic
(PPG) signal communicated by a PPG sensor of a wearable device worn
by a user; detecting a plurality of heartbeats of the user based on
the PPG-signal; determining a heart rate of the user based on at
least the plurality of heartbeats; determining a heart rate
variability (HRV) of the user based on the plurality of heartbeats;
determining a respiration rate of the user based on a low frequency
component of the PPG signal; and determining whether the user is in
a stressed state based on the heart rate, the HRV, and the
respiration rate, wherein determining whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate comprises determining a mathematical model for the
user, causing the heart rate, the HRV, and the respiration rate to
be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs, wherein the
mathematical model comprises a weighted combination of the heart
rate, the respiration rate, and the HRV for the user, wherein the
heart rate is weighted more heavily than the respiration rate or
the HRV.
2. The computer program product of claim 1, wherein the detecting
the plurality of heartbeats comprises detecting the plurality of
heartbeats of the user from a maximum gradient of the PPG
signal.
3. The computer program product of claim 2, wherein detecting the
plurality of heartbeats further comprises performing spline
interpolation on local maxima of the maximum gradient.
4. The computer program product of claim 1, wherein determining
whether the user is in a stressed state based on the heart rate,
the HRV, and the respiration rate comprises determining a stress
level of the user based on the heart rate, the HRV, and the
respiration rate.
5. The computer program product of claim 1, wherein the weighted
combination of heart rate, respiration rate, and heart rate
variability comprises a three feature vector, and causing the
mathematical model to output the determination of whether the user
is in a stressed state comprises analyzing the three feature vector
in a three dimensional vector space to determine whether the three
feature vector breaches one or more thresholds that define one or
more stress zones or volumes in the three dimensional vector space,
the mathematical model configured such that, responsive to at least
a portion of the three feature vector passing through and/or being
bounded by the one or more stress zones or volumes, the
mathematical model determines that the user is in a stressed
state.
6. The computer program product of claim 5, wherein the thresholds
are determined based on user profile information, the user profile
information describing baseline or normal pathological values for
the heart rate, the respiration rate, and the HRV of the user.
7. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by the user, the motion signal conveying
information related to an activity level of the user; and, wherein
determining whether the user is in a stressed state is further
based on the activity level.
8. The computer program product of claim 7, wherein determining
whether the user is in a stressed state includes a determination of
whether the activity level of the user breaches a minimum activity
threshold level.
9. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise causing display of information related to the
determination of whether the user is in a stressed state on a
display of the wearable device and/or a different computing device
associated with the user.
10. The computer program product of claim 9, wherein the operations
performed by the at least one programmable processor further
comprise making multiple determinations of whether the user is in a
stressed state over a period of time, and wherein the causing
display of information related to the determination of whether the
user is in a stressed state comprises causing display of
information related to the multiple determinations of whether the
user is in a stressed state.
11. The computer program product of claim 10, wherein the
operations performed by the at least one programmable processor
further comprise determining an amount of time the user is in the
stressed state during the period of time.
12. The computer program product of claim 11, wherein the
operations performed by the at least one programmable processor
further comprise generating and causing display of a recommendation
for lifestyle changes determined based on the amount of time the
user is in the stressed state.
13. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by a user during a period of time, the motion
signal conveying information related to an activity level of the
user.
14. The computer program product of claim 13, wherein the
operations performed by the at least one programmable processor
further comprise: determining whether the activity level is
indicative of sleep, exercise, and/or normal daily activity; and
causing display of periods of stress, sleep, exercise, and/or
normal daily activity during the period of time.
15. The computer program product of claim 1, wherein the operations
performed by the at least one programmable processor further
comprise, responsive to a determination that the user is in a
stressed state, determining breathing guidance and causing display
of the breathing guidance to the user on a display of the wearable
device to facilitate stress reduction.
16. A wrist worn device configured to determine whether a user is
in a stressed state, the wrist worn device comprising: a
photoplethysmographic (PPG) sensor configured to generate a PPG
signal; at least one programmable processor and a non-transitory,
machine-readable medium storing instructions which, when executed
by the at least one programmable processor, cause the at least one
programmable processor to perform operations comprising: receiving,
at the at least one programmable processor, the PPG signal
communicated by the PPG sensor; detecting a plurality of heartbeats
of the user based on the PPG-signal; determining a heart rate of
the user based on at least the plurality of heartbeats; determining
a heart rate variability (HRV) of the user based on the plurality
of heartbeats; determining a respiration rate of the user based on
a low frequency component of the PPG signal; and determining
whether the user is in a stressed state based on the heart rate,
the HRV, and the respiration rate, wherein determining whether the
user is in a stressed state based on the heart rate, the HRV, and
the respiration rate comprises determining a mathematical model for
the user, causing the heart rate, the HRV, and the respiration rate
to be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs, wherein the
mathematical model comprises a weighted combination of the heart
rate, the respiration rate, and the HRV for the user, wherein the
heart rate is weighted more heavily than the respiration rate or
the HRV; and a user interface controlled by the at least one
programmable processor, the user interface controlled by the at
least one programmable processor to display information related to
the determination of whether the user is in a stressed state.
17. The wrist worn device of claim 16, wherein the detecting the
plurality of heartbeats comprises detecting the plurality of
heartbeats of the user from a maximum gradient of the PPG
signal.
18. The wrist worn device of claim 17, wherein detecting the
plurality of heartbeats further comprises performing spline
interpolation on local maxima of the maximum gradient.
19. The wrist worn device of claim 16, wherein determining whether
the user is in a stressed state based on the heart rate, the HRV,
and the respiration rate comprises determining a stress level of
the user based on the heart rate, the HRV, and the respiration
rate.
20. The wrist worn device of claim 16, wherein the weighted
combination of heart rate, respiration rate, and heart rate
variability comprises a three feature vector, and causing the
mathematical model to output the determination of whether the user
is in a stressed state comprises analyzing the three feature vector
in a three dimensional vector space to determine whether the three
feature vector breaches one or more thresholds that define one or
more stress zones or volumes in the three dimensional vector space,
the mathematical model configured such that, responsive to at least
a portion of the three feature vector passing through and/or being
bounded by the one or more stress zones or volumes, the
mathematical model determines that the user is in a stressed
state.
21. The wrist worn device of claim 20, wherein the thresholds are
determined based on user profile information, the user profile
information describing baseline or normal pathological values for
the heart rate, the respiration rate, and the HRV of the user.
22. The wrist worn device of claim 16, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by the user, the motion signal conveying
information related to an activity level of the user; and, wherein
determining whether the user is in a stressed state is further
based on the activity level.
23. The wrist worn device of claim 22, wherein determining whether
the user is in a stressed state includes a determination of whether
the activity level of the user breaches a minimum activity
threshold level.
24. The wrist worn device of claim 16, wherein the operations
performed by the at least one programmable processor further
comprise causing display of information related to the
determination of whether the user is in a stressed state on a
display of the wearable device and/or a different computing device
associated with the user.
25. The wrist worn device of claim 24, wherein the operations
performed by the at least one programmable processor further
comprise making multiple determinations of whether the user is in a
stressed state over a period of time, and wherein the causing
display of information related to the determination of whether the
user is in a stressed state comprises causing display of
information related to the multiple determinations of whether the
user is in a stressed state.
26. The wrist worn device of claim 25, wherein the operations
performed by the at least one programmable processor further
comprise determining an amount of time the user is in the stressed
state during the period of time.
27. The wrist worn device of claim 26, wherein the operations
performed by the at least one programmable processor further
comprise generating and causing display of a recommendation for
lifestyle changes determined based on the amount of time the user
is in the stressed state.
28. The wrist worn device of claim 16, wherein the operations
performed by the at least one programmable processor further
comprise receiving, at the at least one programmable processor, a
motion signal communicated by an accelerometer sensor of the
wearable device worn by a user during a period of time, the motion
signal conveying information related to an activity level of the
user.
29. The wrist worn device of claim 28, wherein the operations
performed by the at least one programmable processor further
comprise: determining whether the activity level is indicative of
sleep, exercise, and/or normal daily activity; and causing display
of periods of stress, sleep, exercise, and/or normal daily activity
during the period of time.
30. The wrist worn device of claim 16, wherein the operations
performed by the at least one programmable processor further
comprise, responsive to a determination that the user is in a
stressed state, determining breathing guidance and causing display
of the breathing guidance to the user on the display of a wearable
device to facilitate stress reduction.
31. A method for determining whether a user of a wearable device is
in a stressed state, the method comprising: receiving, with at
least one programmable processor, a photoplethysmographic (PPG)
signal communicated by a PPG sensor of the wearable device worn by
the user; detecting a plurality of heartbeats of the user based on
the PPG-signal; determining a heart rate of the user based on at
least the plurality of heartbeats; determining a heart rate
variability (HRV) of the user based on the plurality of heartbeats;
determining a respiration rate of the user based on a low frequency
component of the PPG signal; and determining whether the user is in
a stressed state based on the heart rate, the HRV, and the
respiration rate, wherein determining whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate comprises determining a mathematical model for the
user, causing the heart rate, the HRV, and the respiration rate to
be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs, wherein the
mathematical model comprises a weighted combination of the heart
rate, the respiration rate, and the HRV for the user, wherein the
heart rate is weighted more heavily than the respiration rate or
the HRV.
32. The method of claim 31, wherein the detecting the plurality of
heartbeats comprises detecting the plurality of heartbeats of the
user from a maximum gradient of the PPG signal.
33. The method of claim 32, wherein detecting the plurality of
heartbeats further comprises performing spline interpolation on
local maxima of the maximum gradient.
34. The method of claim 31, wherein the weighted combination of
heart rate, respiration rate, and heart rate variability comprises
a three feature vector, and causing the mathematical model to
output the determination of whether the user is in a stressed state
comprises analyzing the three feature vector in a three dimensional
vector space to determine whether the three feature vector breaches
one or more thresholds that define one or more stress zones or
volumes in the three dimensional vector space, the mathematical
model configured such that, responsive to at least a portion of the
three feature vector passing through and/or being bounded by the
one or more stress zones or volumes, the mathematical model
determines that the user is in a stressed state.
Description
DESCRIPTION OF THE RELATED ART
Disclosures herein relate to monitoring the health of a user and
providing health guidance. For example, electrical and
physiological characteristics of a user's human heart can be
measured using sensors such as electrocardiogram (ECG) sensors or
photoplethysmograph (PPG) sensors. In some circumstances, such
sensors may be included on a wearable device such as a smartwatch.
Signals from such sensors may then be analyzed to determine useful
and informative health states of a patient, such as heart rates,
heart rate variability, particular heart rhythms, and the like.
SUMMARY
Systems, methods and computer software for monitoring the heath of
a user are disclosed herein. In one implementation, a computer
program product is described comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by a processor, may cause the processor to perform operations such
as receiving a photoplethysmographic (PPG) signal communicated by a
PPG sensor of a wearable device worn by a user, detecting a
plurality of heartbeats of the user based on the PPG-signal,
determining a heart rate of the user based on at least the
plurality of heartbeats, determining a heart rate variability (HRV)
of the user based on the plurality of heartbeats, determining a
respiration rate of the user based on a low frequency component of
the PPG signal, and determining whether the user is in a stressed
state based on the heart rate, the HRV, and the respiration
rate.
In some implementations, determining whether the user is in a
stressed state based on the heart rate, the HRV, and the
respiration rate may comprise determining a mathematical model for
the user, causing the heart rate, the HRV, and the respiration rate
to be used as inputs into the mathematical model, and causing the
mathematical model to output the determination of whether the user
is in a stressed state based on the inputs. In some
implementations, the mathematical model may comprise a weighted
combination of the heart rate, the respiration rate, and the HRV
for the user. The heart rate may be weighted more heavily than the
respiration rate or the HRV.
In certain implementations, the weighted combination of heart rate,
respiration rate, and heart rate variability may comprise a three
feature vector and causing the mathematical model to output the
determination of whether the user is in a stressed state may
comprise analyzing the three feature vector in a three dimensional
vector space to determine whether the three feature vector breaches
one or more thresholds that define one or more stress zones or
volumes in the three dimensional vector space. The mathematical
model may be configured such that, responsive to at least a portion
of the three feature vector passing through and/or being bounded by
the one or more stress zones or volumes, the mathematical model
determines that the user is in a stressed state.
In some implementations, the operations performed by the
programmable processor may further comprise causing the display of
information related to the determination of whether the user is in
a stressed state on a display of a wearable device and/or on a
different computing device associated with the user. In some
implementations, the operations may further include making multiple
determinations of whether the user is in a stressed state over a
period of time.
In additional implementations, the operations may further include
receiving a motion signal communicated by an accelerometer on the
wearable device worn by a user during a period of time. The motion
signal may convey information related to an activity level of the
user. In some implementations, the operations performed by the
programmable processor may further including determining whether
the activity level is indicative of sleep, exercise, and/or normal
daily activity; and causing the display of periods of stress,
sleep, exercise, and/or normal daily activity for the period of
time.
In additional implementations, the operations performed by the
programmable processor may further comprise (responsive to a
determination that the user is in a stressed state) determining
breathing guidance and causing display of the breathing guidance to
the user on a display of the wearable device, to facilitate stress
reduction.
As another example, a wrist worn device configured to determine
whether a user is in a stressed state is described. The wrist worn
device may comprise a photoplethysmographic (PPG) sensor, at least
one programmable processor and a non-transitory storage medium, a
user interface, and/or other components. The PPG sensor may be
configured to generate a PPG signal. The non-transitory,
machine-readable medium may store instructions which, when executed
by the at least one programmable processor, cause the programmable
processor to perform operations comprising: receiving, at the at
least one programmable processor, the PPG signal communicated by
the PPG sensor, detecting a plurality of heartbeats of the user
based on the PPG-signal, determining a heart rate of the user based
on at least the plurality of heartbeats, determining a heart rate
variability (HRV) of the user based on the plurality of heartbeats,
determining a respiration rate of the user based on a low frequency
component of the PPG signal, and determining whether the user is in
a stressed state based on the heart rate, the HRV, and the
respiration rate. The user interface may be controlled by the
programmable processor to display information related to the
determination of whether the user is in a stressed state.
Implementations of the current subject matter can include, but are
not limited to, methods consistent with the descriptions provided
herein as well as articles that comprise a tangibly embodied
machine-readable medium operable to cause one or more machines
(e.g., computers, etc.) to result in operations implementing one or
more of the described features. Similarly, computer systems are
also contemplated that may include one or more processors and one
or more memories coupled to the one or more processors. A memory,
which can include a computer-readable storage medium, may include,
encode, store, or the like, one or more programs that cause one or
more processors to perform one or more of the operations described
herein. Computer implemented methods consistent with one or more
implementations of the current subject matter can be implemented by
one or more data processors residing in a single computing system
or across multiple computing systems. Such multiple computing
systems can be connected and can exchange data and/or commands or
other instructions or the like via one or more connections,
including but not limited to a connection over a network (e.g., the
internet, a wireless wide area network, a local area network, a
wide area network, a wired network, or the like), via a direct
connection between one or more of the multiple computing systems,
etc.
The details of one or more variations of the subject matter
described herein are set forth in the accompanying drawings and the
description below. Other features and advantages of the subject
matter described herein will be apparent from the description and
drawings, and from the claims. While certain features of the
currently disclosed subject matter are described for illustrative
purposes in relation to particular implementations, it should be
readily understood that such features are not intended to be
limiting. The claims that follow this disclosure are intended to
define the scope of the protected subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute
a part of this specification, show certain aspects of the subject
matter disclosed herein and, together with the description, help
explain some of the principles associated with the disclosed
implementations. In the drawings,
FIG. 1 illustrates an exemplary system that can provide for the
monitoring of user health characteristics and provide
health-related guidance in accordance with certain aspects of the
present disclosure,
FIG. 2 illustrates an implementation of a user wearable device in
accordance with certain aspects of the present disclosure,
FIG. 3 illustrates an implementation of a communication device in
accordance with certain aspects of the present disclosure,
FIG. 4A illustrates example heartbeat interval data in accordance
with certain aspects of the present disclosure,
FIG. 4B illustrates an example method for heartbeat detection in
accordance with certain aspects of the present disclosure,
FIG. 4C illustrates an example of spline interpolation in
accordance with certain aspects of the present disclosure,
FIG. 5 illustrates an exemplary display of a user's health states
over time, utilizing HRV calculations in accordance with certain
aspects of the present disclosure,
FIG. 6A illustrates a first example of a health information display
of a user wearable device application and/or a communication device
application in accordance with certain aspects of the present
disclosure,
FIG. 6B illustrates a second example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
FIG. 6C illustrates a third example of a health information display
of a user wearable device application and/or a communication device
application in accordance with certain aspects of the present
disclosure,
FIG. 6D illustrates a fourth example of a health information
display of a user wearable device application and/or a
communication device application in accordance with certain aspects
of the present disclosure,
FIG. 6E illustrates a fifth example of a health information display
of a user wearable device application and/or a communication device
application in accordance with certain aspects of the present
disclosure, and
FIG. 7 illustrates an exemplary display that may be used to guide a
user's breathing rate in accordance with certain aspects of the
present disclosure.
DETAILED DESCRIPTION
The subject matter described herein relates to systems, methods and
software for monitoring certain heath states of a user and for
providing the user with guidance. Exemplary systems are described
that can utilize information from sensors to assess various aspects
of user's health, for example, by analyzing relationships between
heart rate, respiration rate, heart rate (and/or pulse rate)
variability, and/or other parameters.
The present systems can be configured to determine levels of stress
experienced by a user and to provide guidance that may facilitate a
reduction in the user's level of stress. For example, if a user
observes (or is alerted to) the occurrence of prolonged periods of
stress, the user can be provided with breathing exercises to reduce
stress--potentially preventing deterioration of the user's health.
In some implementations, the present systems can be configured to
monitor respiration rate, heart rate, HRV/PRV and/or other
parameters to assess the effectiveness of the breathing exercises
and to present feedback to the user so he or she can see the
immediate effect of the exercises on his or her current level of
stress and physiology. This can provide a positive feedback
mechanism that may encourage the user to continue the exercises as
they can directly and quantifiably see the effect on their
physiology.
FIG. 1 illustrates an exemplary system 100 that can provide for the
monitoring of health characteristics of a user (for example, a
human patient, or other living organism) and can provide health
guidance to the user based on the health characteristics
monitoring.
In some implementations, the exemplary system 100 depicted in FIG.
1 may include elements such as user wearable device(s) 108 (e.g., a
smartwatch), communication devices 102, 104, and 106 (e.g., a
mobile phone or PC), user monitoring devices 110 and 112 (e.g., a
separate smart scale or blood glucose monitor), data analysis
device(s) 114, server(s) 116 (e.g., including processor(s) 117 and
database(s) 118), network(s) 120, and/or other components. The
server(s) 116, wearable devices 108, communication devices 102-106,
user monitoring devices 110 and 112, data analytics devices 114
and/or other devices may include communication lines or ports to
enable the exchange of information within a network (e.g., network
120), or within other computing platforms via wired or wireless
techniques (e.g., Ethernet, fiber optics, coaxial cable, WiFi,
Bluetooth, near field communication, or other technologies).
It should be noted that, while one or more operations are described
herein as being performed by particular components of system 100,
those operations may, in some implementations, be performed by
other components of system 100. As an example, while one or more
operations are described herein as being performed by components of
data analysis device(s) 114, those operations may, in other
implementations, be performed by components of the user wearable
device(s) 108, by components of the communications devices 102,
104, and 106, and/or by other components of system 100. In
addition, although many of the devices are shown separately, one or
more components shown in FIG. 1 may be included in and/or coupled
to one more other components shown in FIG. 1. For example, one or
more data analysis devices 114 may be included in one or more
servers 116, one more communication devices 102-106, one or more
user monitoring devices 110 and/or 112, one or more user wearable
devices 108, and/or other components.
The user wearable device(s) 108 may be a smartwatch (for example,
Samsung Gear, Apple Watch, etc.), or any other device that a user
can wear. A user wearable device 108 may include one or more
sensors that are housed by and/or otherwise integrated with the
device. For example, a user wearable device 108 that is a
smartwatch may include motion sensors (for example,
accelerometers), bio-impedance sensors, electrocardiogram (ECG)
sensors, ballistocardiogram sensors, acoustic sensors (for example,
ultrasound sensors), photo plethysmographic (PPG) sensors that can
use light-based technology to sense a rate of blood flow, and other
sensors. The PPG sensor may be configured to generate a PPG signal
and communicate the PPG signal to at least one programmable
processor of wearable device 108 worn by a user, for example.
Wearable device 108 may also be considered herein to include
sensors that are worn on a user's body but not integrated within
the main wearable portion (for example, an ECG sensor worn on a
user's chest that is not integrated with a smartwatch, but which
nevertheless communicates with the smartwatch).
FIG. 2 illustrates wearable device 108, including processing
circuitry 202, sensor(s) 204, wearable user interface 206, wearable
device application 208, and memory 210. As noted, sensor(s) 204 may
include multiple sensors integrated with the main wearable portion
of the device and/or sensors located elsewhere on the user's body.
The wearable device application 208, and signals from sensor(s)
204, may be stored in memory 210.
Wearable user interface 206 is configured to provide an interface
between user wearable device 108 and/or system 100 (FIG. 1) and a
user through which the user may provide information to and receive
information from user wearable device 108 and/or system 100. This
enables data, cues, results, and/or instructions and any other
communicable items, collectively referred to as "information," to
be communicated between a user and one or more of the components of
system 100 shown in FIG. 1 and/or the components of wearable device
108 shown in FIG. 2. A user may interact with wearable user
interface 206, for example, to enter data such as age, height,
weight and gender, or to view measured or calculated metrics such
as heart rate, pulse rate variability, stress level, breathing
guidance, and the like. Examples of interface devices suitable for
inclusion in user wearable user interface 206 comprise a keypad,
buttons, switches, a display screen, a touch screen, speakers, a
microphone, an indicator light, an audible alarm, a tactile
feedback device, and/or other interface devices. In short, any
technique for communicating information with system 100 is
contemplated by the present disclosure as wearable user interface
206.
Wearable device application 208 may run on processing circuitry 202
and perform such operations as receiving signals from sensor(s)
204, calculating various health characteristics, outputting the
display of information, providing health guidance to the user, etc.
Wearable device application 208 and/or processing circuitry 202 are
configured to provide information processing capabilities in
wearable device 108 and/or system 100. Processing circuitry 202 may
comprise one or more of a digital processor, an analog processor,
and a digital circuit designed to process information, an analog
circuit designed to process information, a state machine, and/or
other mechanisms for electronically processing information.
Although processing circuitry 202 is shown in FIG. 2 as a single
entity, this is for illustrative purposes only. In some
implementations, processing circuitry 202 may comprise a plurality
of processing units. These processing units may be physically
located within the same device (e.g., user wearable device 108), or
processing circuitry 202 may represent processing functionality of
a plurality of devices operating in coordination. In some
implementations, processing circuitry 202 is configured to execute
one or more computer program modules. In some implementations,
wearable device application 208 may include the one more computer
program modules (e.g., programming instructions configured to cause
user wearable device 108 to function as described herein).
Processing circuitry 202 may be configured to execute the modules
by software; hardware; firmware; some combination of software,
hardware, and/or firmware; and/or other mechanisms for configuring
processing capabilities.
In some implementations, memory 210 comprises electronic storage
media that electronically stores information. The electronic
storage media of memory 210 may comprise one or both of system
storage that is provided integrally (i.e., substantially
non-removable) with a user wearable device 108 and/or removable
storage that is removably connectable to a user wearable device 108
via, for example, a port (e.g., a USB port, a firewire port, etc.)
or a drive (e.g., a disk drive, etc.). Memory 210 may comprise one
or more of optically readable storage media (e.g., optical disks,
etc.), magnetically readable storage media (e.g., magnetic tape,
magnetic hard drive, etc.), electrical charge-based storage media
(e.g., EPROM, RAM, etc.), solid-state storage media (e.g., flash
drive, etc.), and/or other electronically readable storage media.
Memory 210 may store software algorithms, information determined by
processing circuitry 202 and/or application 208, information
received from a user, and/or other information that enables system
100 to function properly. Memory 210 may be (in whole or in part) a
separate component within a user wearable device 108, or memory 210
may be provided (in whole or in part) integrally with one or more
other components of a user wearable device 108 (e.g., processing
circuitry 202).
In some implementations, processing circuitry 202, wearable device
application 208, memory 210, and/or other components may comprise a
computer program product comprising a non-transitory,
machine-readable medium storing instructions which, when executed
by at least one programmable processor, cause the at least one
programmable processor to perform various operations (e.g., as
described below). The operations may comprise, for example,
receiving, at the at least one programmable processor, the PPG
signal communicated by the PPG sensor.
Wearable device 108 may be calibrated and/or otherwise configured
(or reconfigured) during a calibration period. The calibration may
be caused and/or performed by a user, processing circuitry 202,
user wearable device application 208, and/or other components of
system 100. For example, a user may wear device 108 for a 24-hour
calibration period upon first use to allow for the collection of
user information from sensor(s) 204. The collection of
characteristics such as pulse rate or respiration rate over a
period of time may facilitate device calibration and provide user
information helpful in the future analysis of signals and the
provision of health guidance to the user. In some implementations,
the calibration may be performed while the user is wearing a single
lead ECG or other sensor(s) for reference purposes.
Returning to FIG. 1, communication devices 102, 104, and 106 may
include any type of mobile or fixed device, for example, a desktop
computer, a notebook computer, a smartphone, a tablet, or other
communication device. Users may, for instance, utilize one or more
communication devices 102, 104, and 106 to interact with one
another, with one or more wearable devices, one or more servers, or
other components of system 100.
FIG. 3 illustrates some components of an exemplary communication
device 102, 104, and/or 106 including processing circuitry 302,
memory 304, user interface 306, and communication device
application 308. The processing circuitry 302, memory 304, and user
interface 306 function similarly to the processing circuitry 202,
memory 210, and user interface 206, respectively, in FIG. 2,
although the application and user interface of a communication
device will commonly have greater functionality than that of a
wearable device.
In some implementations, communication device application 308 may
be a mobile application (for example, a smartphone application), or
a web application. The communication device application 308, in
some implementations, can communicate with the user wearable device
application 208 via Bluetooth (or any other method of wired or
wireless communication) and/or may transmit measurements for
archival and post-processing to a cloud-based database (for
example, database(s) 118). The communication device application 308
may aggregate data from the other sensors (for example, from user
monitoring devices 110 and 112), perform pre-transmission
processing locally, and transmit data for further processing or
viewing. In some implementations, processing circuitry 302,
communication device application 308, memory 304, and/or other
components may comprise a computer program product comprising a
non-transitory, machine-readable medium storing instructions which,
when executed by at least one programmable processor, cause the at
least one programmable processor to perform various operations
(e.g., as described below).
Returning to FIG. 1, in some implementations, user monitoring
devices 110 and 112 may include a blood pressure monitoring device
(for example, a blood pressure cuff), a weight monitoring device
(for example, a scale), a blood glucose monitoring device, etc.
User monitoring devices 110 and 112 may measure health states of
the user different from the health states measured by user wearable
device(s) 108.
The health monitoring and guidance systems and methods detailed
herein typically utilize signals coming from one or more sensors
that may be in contact with a user's body and that are sensing
information relevant to the user. Sensors can be integrated with a
wearable device (e.g., a wearable device 108), communicating with a
wearable device, or can instead be separate from a wearable device
and communicating with system 100 through other components.
As discussed further herein, system 100 can include components and
methods for acquiring particular signals, for processing such
signals (e.g., providing noise reduction), and for modifying signal
acquisition methods. Each of these activities may be performed by a
variety of the components of system 100.
In some implementations, a user wearable device 108 may receive an
optical signal such as a pulse signal from optical sensor(s) (e.g.,
sensors 204 in FIG. 2) utilizing, for example, green and/or
infrared wavelengths of light. Wearable device 108 may include
and/or be operatively coupled with an optical PPG sensor (e.g., a
first sensor 204) configured to generate output signals conveying
information related to the heart rate of a user.
Wearable device 108 may also include a sensor to capture a motion
signal that may be used to assess noise or interference resulting
from motion of a user wearing device 108 or to assess other
parameters relevant to health analysis and guidance. The motion
signal may be generated by, for example, one or more accelerometers
(e.g., a second sensor 204) included in and/or otherwise
operatively coupled to user wearable device 108. The one or more
accelerometers may be configured to generate one or more output
signals conveying information related to movement and/or motion of
a user, for example.
In some implementations, processing circuitry 202 (FIG. 2) and/or a
user wearable device application 208 (FIG. 2) may be configured
such that the optical signal, the motion signal, and/or other
signals can be buffered within a memory (for example, memory 210 in
FIG. 2) of the user wearable device(s) 108 for a predetermined time
period, and the optical signal and the motion signal can then be
provided to other processors for the processing of these signals
(for example, processing circuitry 302 of communication device 104
in FIG. 3 or the circuitry of data analysis device(s) 114). As
such, power consumption of the user wearable device(s) 108 may be
conserved or optimized. Alternatively, in some implementations, the
processing circuitry 202 of a user wearable device 108 itself may
be used for processing the optical pulse signal and the motion
signal.
In some implementations, signal collection or acquisition from an
optical sensor (e.g., a sensor 204 shown in FIG. 2) at a 12-50 Hz
sampling frequency (for example) may be used. This sampling
frequency may be used when there is a general absence of user
motion combined with low-levels of perfusion and low ambient light
interference and/or at other times. In some implementations,
processing circuitry 202 and/or user wearable device application
(FIG. 2) are configured to automatically determine (e.g., as
described below) the sampling frequency based on various conditions
that can affect the output signals from the sensors including, but
not limited to, the motion of a user wearing the device 108.
Signal processing challenges caused by user motion may be overcome
by adjusting various parameters relating to signal acquisition. For
example, optical sensor performance can be adjusted when activity
is detected by a motion sensor (e.g., a three-axis accelerometer).
In some implementations, if motion above a specific threshold is
detected, any or all of the following acquisition parameters of an
optical sensor can be adjusted to overcome the level of noise and
to improve the accuracy of health characteristic determination: (i)
sampling frequency, (ii) LED power, and/or (iii) pulses per sample.
Conversely, in some implementations, if motion below a specific
threshold is detected, then each of these acquisition parameters
may be adjusted to maintain a specific level of performance and
measurement precision, while also conserving power.
In the general absence of user motion, a sampling frequency of
approximately 20 Hz may be appropriate but, with increased user
movement, for example, sampling can be increased to 100 or 200 Hz
or, if necessary, up to 1000 Hz or more to ensure that signals are
received that are useful for the analysis of user health
characteristics.
Various health characteristics of a user may be determined
utilizing information in the output signals from sensors discussed
herein (e.g., by processing circuitry 202 and/or application 208 of
a user wearable device 108 shown in FIG. 2, by processing circuitry
302 and/or application 308 of a communication device 102-106 shown
in FIG. 3, by a data analysis device 114, by a server 116, and/or
other components of system 100). As one example, sensors associated
with a user wearable device 108, such as a smartwatch, may be
utilized to determine a user's heart rate, respiration rate, pulse
rate variability (PRV), heart rate variability (HRV), and/or other
health characteristics. Heart rate is typically described as the
number of heartbeats per minute, while HRV and PRV both refer to
variability of time intervals between beats. HRV typically refers
to variability measurements based on electrocardiography and can be
derived from R-R intervals in the standard PQRS waveform. An HRV
determination may utilize an ECG sensor (e.g., a user monitoring
device 110 and/or 112) on a user that may communicate with wearable
device 108. PRV, on the other hand, typically refers to variability
determinations based on sensors placed proximal to peripheral
arteries, such as optical sensor(s) on a user's wrist that provide
a peripheral pulse waveform without the morphology information seen
in an ECG signal.
User health characteristics may be determined through signal
analysis performed on user wearable device 108 or other components
of system 100 such as communication device 102 or data analysis
device 114, or the analysis may be performed on more than one
component of system 100.
As shown in FIG. 4A, in some implementations, a received signal can
be an ECG signal 400, and the time 402 at which each heartbeat has
occurred can be determined, for example, from each R spike 402 in
the waveform of signal 400. FIG. 4A illustrates an R-R interval 409
(e.g., an amount of time between beats) for reference.
Alternatively, the time at which each heartbeat has occurred may be
determined from a PPG signal. FIG. 4A also illustrates a sample PPG
signal 410 and a sample ECG signal 412 for reference.
As illustrated in FIG. 4B, one method for determining precise
heartbeat times from a PPG signal is to determine the maximum
points in a PPG gradient plot 450. FIG. 4B illustrates an exemplary
method for such detection. In some implementations, processing
circuitry 202, wearable device application 208, memory 210, and/or
other components may be configured to detect a plurality of
heartbeats of the user based on the PPG signal. In some
implementations, detecting the plurality of heartbeats comprises
detecting the plurality of heartbeats of the user from a maximum
gradient of the PPG signal. As shown in FIG. 4B, in some
implementations, plot 450 may include an original PPG pulsation
signal (e.g., at 30 Hz) 452 and a corresponding gradient 454 of
signal 452. Determining heartbeat times from the PPG signal may
include determining the locations 456 where gradient 454 is at a
maximum. These locations can correspond to individual beats. A
heart rate of the user may be determined based on at least the
plurality of the determined heartbeats.
As shown, detecting the plurality of heartbeats may include
performing spline interpolation on local maxima of the maximum
gradient. For example, the spline interpolation 458 may be
performed at individual maximums 456 to accurately determine the
timing of a beat. Improved resolution for such heartbeat
determinations may be obtained through a variety of methods
including, for example, spline interpolation 458 as shown in FIGS.
4B and 4C. As shown in FIG. 4C, a given beat location 456 may be
determined based on gradient 454 maximums using a spline
interpolation 480. FIG. 4C illustrates, at PPG gradient 454 maximum
y(t) 456, spline interpolation 480 for samples y(t-1), y(t), and
y(t+1). System 100 (FIG. 1) may be configured such that y(t-1),
y(t), and y(t+1) are kept in memory. In this example, if a sampling
rate was 25 Hz, time between each sample is 40 ms. Spline
interpolation 480 is performed to determine a location of an R
spike (t.sub.2) with three points of a cubic natural spline.
In certain implementations, system 100 (e.g., via any of the
processing and/or data analysis components described above related
to FIG. 1-3) may be configured to facilitate a change and/or an
adjustment in sampling rate (e.g., by any of the sensors described
above) associated with the sensor output signals. In some
implementations, the sampling rate may be increased at or near
portions of a signal that correspond to a beat (e.g., at or near
times that correspond to beat locations 456) and/or decreased in
off peak and/or valley areas of plot 450 and/or gradient 454. These
changes and/or adjustments in sampling rate may facilitate power
and/or data storage space savings.
In some implementations, system 100 (e.g., via any of the
processing and/or data analysis components described above related
to FIG. 1-3) may be configured to identify noisy portions of a
signal and disregard data associated with the noisy portions of the
signal. System 100 may be configured to identify the noisy portions
of a signal based on information from an accelerometer indicating
elevated motion levels, and/or other information. For example,
system 100 may be configured to determine that motion levels have
breached a motion threshold level and exclude signal data for a
period of time when motion levels remain in breach of the motion
threshold. The motion threshold level may be determined at
manufacture of system 100, determined and/or adjusted by a user
(e.g., via a user interface described herein), determined based on
prior monitoring of the user, determined based on historical data
in medical records associated with the user, and/or may be
determined in other ways. In some implementations, responsive to
identifying a noisy portion of a signal, system 100 can be
configured to perform signal enhancement and/or signal
reconstruction operations (e.g., on a PPG sensor signal). After
received signals are analyzed, and precise heartbeat times have
been determined (for example, over a sample time of 10 seconds), a
heart rate in beats per minute can be determined.
Heart rate variability (HRV) and/or pulse rate variability (PRV)
may be determined based on the plurality of heartbeats (e.g.,
determined as described above). In some implementations, PRV and
HRV may be described as time variances between successive
heartbeats in milliseconds (and/or other increments). Typically, a
number of time deltas between beats are determined and then
statistical analysis is performed to arrive at various indications
of HRV/PRV for the timeframe being examined. This analysis may be
done in the time domain, the frequency domain, and the non-linear
domain (e.g., by any of the processing and/or data analysis
components described above).
Humans breathe (inhale and exhale) anywhere from 6-20 times per
minute (or in extreme cases even higher). In some implementations,
a user's respiration rate can be determined (e.g., by any of the
processing and/or data analysis components described above) from a
PPG signal through examination, for example, of the frequency
domain, considering slight heart rate increases observed every time
a person inhales and slight heart rate decreases every time a
person exhales. These small shifts in heart rate, as calculated,
for example, through the time delta between successive beats, can
be analyzed to determine respiration rate.
In some implementations, system 100 may be configured to analyze a
low frequency component of a PPG signal to determine the
respiration rate of a user such that the respiration rate of the
user may be determined based on the low frequency component of the
PPG signal. In some implementations, there exists a low frequency
component of the PPG signal that is different than a high frequency
component of the PPG signal. The low frequency component of the PPG
signal may be related to respiration (e.g., inhalation and
exhalation) and the high frequency component of the PPG signal may
be related to the plurality of heartbeats. For example, a PPG
signal may include a plurality of high frequency individual
oscillations (that correspond to individual heartbeats) overlaid on
and/or part of a plurality of low frequency oscillations that
correspond to respiration. In some implementations, an increasing
portion of a given low frequency oscillation may correspond to an
inhalation, and a decreasing portion of the given low frequency
oscillation may correspond to an exhalation.
Returning to FIG. 1, user characteristics such as those described
above can aid the present system in performing various health
assessments. For example, on a basic level, a user's resting heart
rate, in conjunction with some background information, can be used
by system 100 to provide a general assessment of a user's health.
Such assessments may be made through calculations on a wearable
device 108, a communication device 104, or other devices in system
100.
Subsequent to such health assessments, system 100 may provide
guidance to a user through various outputs to a user interface 206
or 306, for example. Such guidance may be in the form of
information, or it may provide feedback in a manner designed to
alter user behavior, as discussed further below. In addition,
system 100 may be configured to provide information to a health
professional or other individual, or to store or analyze
information in particular databases or servers.
To facilitate health assessments, system 100 can develop a profile
for a user that provides a composite characterization of the state
of the user's autonomic nervous system (ANS) tone, reflecting the
overall influence of sympathetic and parasympathetic nervous
systems on the cardiovascular system.
The profile for a user can be established as a baseline, typically
prior to the start of an assessment or prolonged measurement. This
can include (but is not limited to) facilitation of entry and/or
selection of information related to a user's age, gender, height,
weight, BMI, and/or other information; determination (e.g., based
on the information in the output signals from one or more of the
sensors described above) minimum heart rate (if known or previously
measured), maximum heart rate (if known or previously measured),
HRV statistics in the time domain (e.g., RMSSD--Root Mean Square of
the Successive Differences, and pNN50, and the Mean Absolute
Deviation can be evaluated to determine the normal range for a
user), HRV statistics in the non-linear domain (e.g., Low Frequency
Power and High Frequency Power to measure the parameters that
effect HRV and the cardiac cycle that usually occur at a frequency
lower than heart rate (namely respiration rate)), a determination
of whether a user experiences atrial fibrillation, and/or other
information.
The parameters for the profile can be received from the user
wearable device(s) 108, communication devices 102, 104, and 106,
server(s) 116, user monitoring devices 110 and 112, and/or other
components of the system 100. For example, personal data such as
height, weight, and age of the user can be received from
communication device 102, and the blood pressure levels, weight
levels, blood glucose levels, etc., can be received from user
monitoring devices 110 and 112. Additionally, any historical data
of the user (for example, past medical records) can be received
from database(s) 118, for example. In some implementations, the
server(s) 116 may include database(s) 118 that store user data (for
example, historical data of the user, including past medical
records) and processor(s) 117 that authenticate user wearable
device(s) 108 and communication devices 102, 104, and 106.
Additional parameters for a profile can be derived from a
combination of the entered profile information and additional
measurement. For example, the Mean Absolute Deviation (MAD) is a
calculation of the amount of deviation from the mean heart rate
over some predetermined period of time (for example, a day, a week,
or a month) indicating an amount of variation in the beat-to-beat
intervals (as measured as the time in milliseconds between pulse
peaks).
Once real-time user characteristics such as heart rate, HRV, PRV
and respiration rate are determined, system 100 is configured to
utilize such characteristics to assess various aspects of user's
health. For example, by determining relationships between
characteristics, system 100 can determine the level of stress
experienced by a user. Determining whether the user is in a
stressed state may be based on the heart rate, the HRV, the
respiration rate, and/or other characteristics of the user. A
stressed state may be and/or be related to a condition of the user
in which the user is feeling anxious, exasperated, concerned,
pressured, tense, and/or otherwise emotionally strained.
In some implementations, determining whether the user is in a
stressed state may include determining a particular stress level of
the user, which may also be based on the heart rate, HRV, and
respiration rate (and/or other characteristics). The stress level
may be a value and/or other indicator assigned to an amount of
stress felt by the user, and/or an intensity of the stressed state
of the user.
A human's autonomic nervous system (ANS) includes two primary
components: the sympathetic and the parasympathetic nervous system.
When a user experiences physical, mental, or emotional stress, the
sympathetic nervous system is stimulated to enable the user to
adapt to the stress. Conversely, when an individual experiences a
relaxation response (or stress recovery), the parasympathetic
nervous system is activated to enable the individual to recover
from the stress.
Acute stress may be defined as short periods of intense
physiological expression or adaptation to real or perceived threats
to a user. Acute stress can be a highly effective response to a
threat (for example, enabling fight or flight behaviors) and can be
life-preserving. Conversely, chronic stress is a prolonged or
habitual state of stress, even after dissipation of a threat.
Chronic stress can hyper-activate the inflammatory system and HPA
(Hypothalamus Pituitary Axis), causing the sympathetic nervous
system (Fight or Flight) to become overactive and inhibit the
parasympathetic system (Rest and Digest), which can prevent
recovery, an essential part of the healing process.
When the heart rate of a user is measured and HRV or PRV are
determined, those values can be compared (e.g., by any of the
processing and/or data analysis components described above) to
values of the heart rate, respiration rate, HRV/PRV, atrial
fibrillation, etc., previously included in the user profile. For
example, when the respiration rate/heart rate/HRV/PRV of a user
deviates from typical pathological values by a predetermined
threshold included in the user profile, system 100 may determine
that the user is experiencing a certain level of stress. For
example, if a user is experiencing a heart rate of 60 bpm, while
their minimum Heart Rate (lowest recorded resting heart rate) is 40
bpm, then 60 bpm may indicate a state of stress with a certain
stress level, especially if HRV/PRV is noted to be decreased and
respiration rate is unchanged or increased. Conversely, for a user
with a minimum heart rate of 56 bpm, a current heart rate of 60 bpm
likely means a state of recovery, with a low level of stress. Other
parameters may be used to support such assessments.
It should be noted that system 100 is configured to account for
pathological irregularities when determining whether a user is
experiencing stress. For example, if a user is prone to atrial
fibrillation, and this condition is indicated in the user's
profile, system 100 is configured to account for (e.g., subtract
out the effect of) this pathological atrial fibrillation when
making a stress determination. Accounting for other similar
pathological irregularities is contemplated.
In addition to determining that a user is experiencing stress, a
particular level of the stress can be determined. For example, if a
user is currently experiencing a heart rate of 72 bpm though their
minimum heart rate (lowest recorded resting heart rate) is 40 bpm,
then 72 bpm may indicate a low level of stress. Conversely, if a
user is currently experiencing a heart rate of 112 bpm when their
minimum heart rate is 40 bpm, then 112 bpm may indicate a high
level of stress.
In some implementations, determining whether the user is in a
stressed state based on the heart rate, the HRV, the respiration
rate, and/or other characteristics can include comprises
determining a mathematical model for the user, causing the heart
rate, the HRV, the respiration rate, and/or the other
characteristics to be used as inputs into the mathematical model,
and causing the mathematical model to output the determination of
whether the user is in a stressed state based on the inputs. For
example, using the profile, a mathematical model can be built
(e.g., by any of the processing and/or data analysis components
described above), based both on the personal characteristics of the
user and population norms for specific physiological functions.
Continuing with the example, the normal resting heart rate range
(which varies by age and gender) is 55-90 bpm. Maximum heart rate
may be calculated as follows: (a) for men over age 30: 207 minus
70% of age, and (b) for women over age 35: 206 minus 88% of age. In
other words, a mathematical model can be built based on personal
data corresponding to the user and other data corresponding to
population norms, and the user's current stress level can be
determined based on a comparison of the user's current heart rate,
HRV, PRV, and/or respiration rate to the mathematical model. The
mathematical model can be updated when the profile is updated with
additional data.
In some implementations, stress related determinations can be
further responsive to information in output signals from a motion
sensor (e.g., an accelerometer as described above) so that
determining a stress level may be based on the heart rate, the HRV,
the respiration rate and an activity level determined at least in
part from a motion sensor. In these types of implementations, the
stress determination may also include determination of whether the
activity level of the user breaches a minimum activity threshold
level, for example, to determine whether the user is awake or
asleep. Such minimum activity threshold levels may be determined at
the manufacture of system 100, determined and/or adjusted by a user
(e.g., using a user interface as described above), determined based
on the user profile data for a user, and/or determined in other
ways.
In some implementations, the mathematical model can include a
weighted combination of heart rate, respiration rate, and heart
rate variability for a user. In some implementations, heart rate
may be weighted more heavily than respiration rate or heart rate
variability.
In some implementations, the weighted combination of heart rate,
respiration rate, and heart rate variability can comprise a three
feature vector (though other dimensional vectors are contemplated).
System 100 may be configured to analyze the features of the vector
as they vary (e.g., relative to corresponding thresholds) for a
user. Causing the mathematical model to output the determination of
whether the user is in a stressed state can include analyzing the
three (for example) feature vector in a three (for example)
dimensional vector space to determine whether the vector breaches
one or more thresholds that define one or more stress zones or
volumes in the three dimensional vector space. In this way, the
vector may be analyzed in vector space to determine whether a user
is stressed and/or to determine a stress level for the user.
These thresholds may be determined based on user profile
information and/or other information. The user profile information
may describe baseline or normal pathological values for the heart
rate, the respiration rate, the HRV, and/or other characteristics
of the user. For example, thresholds for individual features (e.g.,
heart rate, respiration rate, heart rate variability) may be
determined. The thresholds may be determined based on the profile
information as described above (e.g., based on baseline or normal
pathological values (e.g., an overnight heart rate) for individual
features for example), real time or near real time information in
sensor output signals, and/or other information. The thresholds for
individual features may be used in vector space to define one or
more stress zones or volumes. The mathematical model may be
configured such that, responsive to at least a portion of the
(e.g., three) feature vector passing through and/or being bounded
by the one or more stress zones, the mathematical model may
determine that the user is stressed.
Similarly, the mathematical model may be configured to determine a
particular stress level based on a position of the vector relative
to one or more of the thresholds, a magnitude and/or length of the
vector, and/or other information. For example, the mathematical
model may output a length of the vector that is, or is indicative
of, the stress level. As another example, the model may output an
indication of a relative distance between the vector (or an end of
the vector) and one or more of the thresholds that is indicative of
the stress level.
The amount of time spent in a sympathetic dominant (stress) state
relative to the time spent in a parasympathetic dominant state, for
a given timeframe (for example, a day or a week), can be derived
from regular measurement of the user's current heart rate, HRV,
PRV, and/or respiration rate (e.g., using the mathematical model
described above)--allowing the user a key insight into their health
and providing an opportunity for intervention if the sympathetic
stress state is dominant for too long. This is evident when the net
time difference between the two states indicates an on-going net
depletion of resources that needs to be replenished (or recovered)
through stress management intervention including, for example:
structured breathing exercises, meditation, higher quality sleep,
and (in most cases other than stress caused by over-training)
increased physical activity.
Other lifestyle changes can also enhance recovery and reduce
chronic stress. These include, but are not limited to, reduction in
caffeine consumption after mid-afternoon, minimizing use of
electronics and physical exercise just prior to bed, and
improvements in diet by focusing on reductions in sugars and
processed food and increases in plant-based foods.
Relative to a user's own cardiovascular and ANS performance,
increases in heart rate and respiration rate, accompanied with a
decrease in Heart Rate Variability, are a general indication of
acute stress. If maintained for prolonged periods of time, this
relationship indicates potentially excessive chronic stress.
Real-time measurement of these parameters allows for effective
intervention to reduce chronic stress and potentially reduce
susceptibility to chronic diseases triggered by unmanaged or
uncontrolled prolonged periods of chronic stress.
System 100 can be configured to provide graphical displays of
information to assist with the assessment of a user's health. For
example, operations performed by the a programmable processor
(e.g., processing circuitry 202, wearable device application 208,
memory 210, and/or other components) may include causing display of
information related to the determination of whether a user is in a
stressed state on a display of wearable device 108 and/or a
different computing device (e.g., communication devices 102-106)
associated with the user. In some implementations, the operations
can include making multiple individual determinations of whether
the user is in a stressed state (e.g., as described above) in an
ongoing manner for a period of time. In some implementations,
causing display of the information related to the determination of
whether the user is in a stressed state can include causing display
of information related to the multiple individual determinations of
whether the user is in a stressed state. In some implementations,
determining whether the user is in a stressed state includes
determining an amount of time the user is in the stressed state
during the period of time.
An example of a display utilizing a user's health and/or activity
states over time is illustrated in FIG. 5. FIG. 5 illustrates
display 500 for a hypothetical period of time for a user. In this
example, measurements were taken and calculations made (as
described herein) throughout the day (e.g., from 00:00:00 to
23:59:59). In some implementations, the operations performed by the
at least one programmable processor (e.g., processing circuitry
202, wearable device application 208, memory 210, and/or other
components) can include determining whether an activity level is
indicative of sleep, exercise, and/or normal daily activity; and
causing the display of periods of stress, sleep, exercise, and/or
normal daily activity. For example, exemplary display 500 in FIG. 5
indicates health and/or activity states including "Activity" 502,
"Recovery" 504, "Exercise Recovery" 506, and "Stress" 508.
Specifically, display 500 illustrates: activity 510 (for example,
from 8 pm to 9 pm), recovery 512 (for example, from midnight to 8
am), stress 514 (for example, from 4 pm to 7 pm), and exercise
recovery 516 at about 3 pm. From midnight to 8 am, display 500 of
FIG. 5 illustrates mostly a recovery state. From 11 am to 3 pm,
display 500 illustrates mixed stress and recovery states throughout
the workday; from 4 pm to 7 pm, display 500 of FIG. 5 illustrates
low-to-mid level stress intensity (for example, because of a
marathon phone conference); and from 8 pm to 9 pm, display 500
illustrates an exercise activity (for example, exercising on an
elliptical trainer at the gym).
In one implementation, the amplitude of the states shown in display
500 of FIG. 5 may be calculated as proportional to the inverse of
the current heart rate minus the minimum heart rate (for a given
user), amplified or reduced by the degree of HRV and the rate of
respiration for the user. As the current heart rate nears the
minimum, and HRV and respiration rate are appropriate for recovery,
the amplitude will approach the maximum. The converse would be true
for an exemplary stress amplitude calculation.
At night, primarily during sleep, heart rate and respiration rate
are lowered and HRV (or similarly PRV) increases relative to
periods of daytime stress. Even during the night, however, movement
or wakefulness can be detected, and recorded for review by the
user. This can provide insight into both the quantity (amount of
time) and quality (amount of recovery) of sleep. Unmanaged chronic
stress can cause reductions in both the quantity and the quality of
sleep. The deeper a user's relaxation during sleep, the higher the
level of recovery will be indicated.
Accelerometer data can be examined to determine if the user is
active and, if so, can be used to determine an activity intensity
value. If the patient is not active, then system 100 may determine
if the user is in recovery based on factors such as a lower heart
rate, an increased HRV/PRV, and a reduced respiration rate
(relative to the patient's normal ranges for each). If the user is
not active and not in recovery, system 100 can determine that the
user is in a state of stress.
In one example, a user may be determined to be in recovery if he
has a minimum heart rate of 47, is currently experiencing a heart
rate of 50, while not being active, and demonstrates increased
heart rate variability and a reduced respiration rate. As described
above, in this example, the amplitude of the recovery indication in
a health state graph such as FIG. 5 may be calculated as
proportional to the inverse of the current heart rate minus the
minimum heart rate amplified or reduced by the degree of HRV and
rate of respiration for the person. As the current heart rate nears
the minimum, and HRV and respiration rate are appropriate for
recovery, the amplitude will approach the maximum. The converse
would be true for an exemplary stress amplitude calculation.
System 100 can be configured to display various health assessment
data gathered and/or calculated, as previously described. For
example, timelines of health assessment data may be displayed on
wearable device 108, or communication device 102, through their
respective wearable device application 208 or communication device
application 308 (see, e.g., FIGS. 6A-6E).
For example, as shown in FIG. 6A, a view 600 of health assessment
data may be displayed on wearable device 108. Similar to display
500 shown in FIG. 5, view 600 comprises a visual indication of
recent periods of stress 602 and non-stress 604. FIG. 6B
illustrates a daily summary view 610 for a busy Thursday workday
(for example). View 610 includes an indication of sleep quality
612, a percentage of the day that stress levels were high 614, and
a timeline that displays heath and/or activity state by time of day
616.
FIG. 6C illustrates an example view 620 of health assessment data
that includes an annotated timeline 622 of health and/or activity
states. Annotated timeline 622 indicates when a user was in states
of stress 624, recovery 626, physical activity 628, and light
physical activity 630. Annotated timeline 622 can be overlaid with
a heart rate indication 632 for the user. Annotations on annotated
timeline 622 can include advice and/or summary windows 634 that
provide advice and/or activity summaries to the user, and time
stamp graphics 636 or messages 638 that indicate various
events.
FIG. 6D illustrates a multi-day summary view 650 for three
successive days (Thursday, Friday, Saturday). View 650 includes a
view 651 similar to view 610 described above, and additional
similar views 652 and 654 for Friday and Saturday. Views 651, 652,
and 654 include indications of sleep quality 660, 662, 664,
percentages of the day that stress levels were high 668, 670, 672,
and annotated timelines 674, 676, 678, that display heath and/or
activity state by time of day, along with summary and/or advice
windows 680 (e.g., similar to those shown in FIG. 6C).
FIG. 6E illustrates a lifestyle assessment summary view 690. View
690 includes a user characteristics summary portion 692, additional
information 694, a timeline 696 that indicates when a user was in
states of stress 624, recovery 626, physical activity 628, and
light physical activity 630. View 690 also illustrates a body
resources portion 698 indicating whether the user's body resources
increased or decreased over a period of three days.
In one implementation, the user wearable device application 208 can
be programmed to update a stress/recovery/sleep/activity timeline
on a beat by beat basis to ensure that it displays immediate health
assessment information both on demand as requested by the user, and
when actionable information is detected. Actionable information may
include the detection of specific stress events that would benefit
from interventions or the detection and display of activity or
recovery events that can re-enforce positive behavior performed by
a user. The wearable device 108 may also be programmed to trigger
haptic feedback to the user in the form of a mild vibration along
with a concise message suggesting actions the user may perform
depending on the type of detected event.
System 100 can be configured to provide additional, more detailed
information on a communication device 102 (e.g., a mobile phone or
computer), for example. In one example, illustrated in FIG. 6B,
communication device 102 may display a detailed health state graph
for a particular day. Such a display may indicate varying user
states such as stress, recovery and physical activity in different
colors on a bar graph and may indicate the percentage of the day
spent in each state to allow a user to understand just how taxing a
particular day may have been on his or her body.
The exemplary display of FIG. 6B illustrates that periods of
recovery, stress or physical activity can be denoted by different
colored columns (e.g., green, red and blue) or shading patterns,
respectively. In addition to color-coded and/or shaded bar charts,
additional assessment information can be provided in the display,
for example, warnings regarding poor sleep quality/duration can be
given, as well as positive feedback relating to exercise.
FIG. 6C illustrates an additional exemplary display including more
detailed information. Heart rate throughout the day can be
displayed, as well as indications of light or strenuous physical
activity and commentary regarding the health effects of various
states observed during the day. In addition, icons such as a
suitcase or a bed, along with corresponding bars across the time
axis, can be displayed as the result of user-entered journaling of
activities. For example, a suitcase may indicate a commute to or
from work, and a bed may indicate sleep.
FIG. 6D illustrates an additional exemplary display showing a user
how his or her body has been affected by activities over a number
of days. In addition to bar charts showing the user's states
throughout a few days and nights, additional indications can be
provided regarding percentages of stress versus recovery state,
sleep quality and exercise activity.
FIG. 6E illustrates an additional exemplary display showing an
overall assessment of a user's body resources over time, indicating
periods of resource depletion and resource recovery--and showing a
cumulative state over time that may be determined by summation of
the additive values of recovery, less the subtractive values of
stress.
In some implementations, the operations performed by the at least
one programmable processor (e.g., processing circuitry 202,
wearable device application 208, memory 210, and/or other
components) may include generating and causing display of a
recommendation for lifestyle changes determined by the amount of
time the user is in the stressed state and/or other information. In
some implementations, the operations performed by the a processor
can further include, responsive to a determination that the user is
in a stressed state, determining breathing guidance and causing
display of the breathing guidance to the user on the display of a
wearable device to facilitate stress reduction.
For example, system 100 may be designed to alert a user to specific
events, for example, with haptic feedback through wearable device
108. In one example, an alert may be given when a period of high
stress is observed. After such an alert, health-related guidance
may be given to the user, such as breathing guidance.
An effective intervention to reduce stress and potentially reduce
susceptibility to chronic diseases is to optimize breathing during
periods of prolonged stress. An optimal respiration rate for an
average person not currently experiencing respiratory health issues
can be considered less than 10 breaths per minute, although this
level can be personalized given respiration patterns and health
conditions of a particular user.
Breathing guidance can be displayed to a user via a display on a
user wearable device 108 or a display of communication device 102.
The breathing guidance can be presented to the user in real-time as
needed or presented on-demand. For example, when system 100
determines that the user's stress level is above a predetermined
threshold for a prolonged period of time, the system can provide
breathing guidance to the user. Alternatively, the user and/or a
medical professional can request that breathing guidance be
provided at predetermined intervals of time.
Components of system 100 (for example, the display on wearable
device 108) can be configured to display graphical breathing
guidance to user. FIG. 7 illustrates a simple exemplary display 703
that may be shown on wearable device 108. Such a display can be
synchronized to a user's breath, or to a desired reduced breathing
rate. In the example shown in FIG. 7, inner circular shape 701 may
expand and/or contract in diameter relative to outer circular shape
702 to indicate to the user when to breath in and/or out for
example. This exemplary display is not intended to be limiting.
In some implementations, a user's PRV and/or HRV, baseline
pathological (e.g., user profile) information, and/or other
information can be used to determine an optimal breathing rate for
the user in view of the user's current respiration rate. When
activated through a set of programmable triggers or manually
activated by the user, breathing guidance can be determined based
on the current breathing rate of the user (e.g., as determined from
the user's respiration rate derived through frequency domain
analysis of PRV statistics as measured from an optical sensor).
System 100 can then be programmed to select, as breathing guidance,
a breathing rate that is slightly slower (e.g., 1-2 breaths per
minute slower) than the current respiration rate. The user's heart
rate and HRV/PRV are continuously monitored throughout the
breathing guidance and, as the intensity of the current stress
level of the user is reduced, the breathing guidance can further
reduce the desired breaths per minute until either a) breaths per
minute is less than 10, or b) heart rate and/or HRV/PRV are reduced
appropriately.
Given that the respiration rate, heart rate, HRV and PRV are being
monitored, the effectiveness of the breathing exercise can be
determined. Ideally, there is a positive and quantifiable change in
heart rate, respiration rate, and HRV/PRV during the breathing
exercises. If the exercise is not effective in altering these
parameters, the amount of time inhaling or exhaling can be
increased or modified, along with guidance on the optimal
inhalation and exhalation techniques. This may prevent instances of
over breathing, which may inhibit the desired relaxation effect of
the breathing exercise.
Using such methods, system 100 can assess both the effectiveness of
breathing exercises, and present feedback to the user performing
the exercise, so he or she can see the immediate effect of the
exercise on their current level of stress and physiology. In some
implementations, system 100 can be configured to cause display of a
goal respiration rate and a current respiration rate on a
continuum. In other implementations, system 100 can be configured
to cause display of individual goal/actual inhalation times and/or
goal/actual exhalation times. This provides a positive feedback
mechanism that may encourage the user to continue the exercise, as
they can directly and quantifiably see the effect on their
physiology, and it may therefore help to develop an effective tool
for stress management. When a user observes (or is alerted) to the
occurrence of prolonged periods of stress, the user can immediately
remedy with breathing intervention methods to reduce stress,
potentially preventing deterioration of health, and enhancing
wellbeing.
Although a few embodiments have been described in detail above,
other modifications are possible. The present disclosure
contemplates that the calculations disclosed in the embodiments
herein may be performed in a number of ways, applying the same
concepts taught herein, and that such calculations are equivalent
to the embodiments disclosed.
One or more aspects or features of the subject matter described
herein can be realized in digital electronic circuitry, integrated
circuitry, specially designed application specific integrated
circuits (ASICs), field programmable gate arrays (FPGAs) computer
hardware, firmware, software, and/or combinations thereof. These
various aspects or features can include implementation in one or
more computer programs that are executable and/or interpretable on
a programmable system including at least one programmable
processor, which can be special or general purpose, coupled to
receive data and instructions from, and to transmit data and
instructions to, a storage system, at least one input device, and
at least one output device. The programmable system or computing
system may include clients and servers. A client and server are
generally remote from each other and typically interact through a
communication network. The relationship of client and server arises
by virtue of computer programs running on the respective computers
and having a client-server relationship to each other.
These computer programs, which can also be referred to programs,
software, software applications, applications, components, modules,
or code, include machine instructions for a programmable processor,
and can be implemented in a high-level procedural language, an
object-oriented programming language, a functional programming
language, a logical programming language, and/or in
assembly/machine language. As used herein, the term
"machine-readable medium" (or "computer readable medium") refers to
any computer program product, apparatus and/or device, such as for
example magnetic discs, optical disks, memory, and Programmable
Logic Devices (PLDs), used to provide machine instructions and/or
data to a programmable processor, including a machine-readable
medium that receives machine instructions as a machine-readable
signal. The term "machine-readable signal" (or "computer readable
signal") refers to any signal used to provide machine instructions
and/or data to a programmable processor. The machine-readable
medium can store such machine instructions non-transitorily, such
as for example as would a non-transient solid-state memory or a
magnetic hard drive or any equivalent storage medium. The
machine-readable medium can alternatively or additionally store
such machine instructions in a transient manner, such as for
example as would a processor cache or other random access memory
associated with one or more physical processor cores.
To provide for interaction with a user, one or more aspects or
features of the subject matter described herein can be implemented
on a computer having a display device, such as for example a touch
screen, a cathode ray tube (CRT), or a liquid crystal display (LCD)
or a light emitting diode (LED) monitor for displaying information
to the user and a keyboard and a pointing device, such as for
example a mouse or a trackball, by which the user may provide input
to the computer. Other kinds of devices can be used to provide for
interaction with a user as well. For example, feedback provided to
the user can be any form of sensory feedback, such as for example
visual feedback, auditory feedback, or tactile feedback; and input
from the user may be received in any form, including, but not
limited to, acoustic, speech, or tactile input. Other possible
input devices include, but are not limited to, touch screens or
other touch-sensitive devices such as single or multi-point
resistive or capacitive trackpads, voice recognition hardware and
software, optical scanners, optical pointers, digital image capture
devices and associated interpretation software, and the like.
In the descriptions above and in the claims, phrases such as "at
least one of" or "one or more of" may occur followed by a
conjunctive list of elements or features. The term "and/or" may
also occur in a list of two or more elements or features. Unless
otherwise implicitly or explicitly contradicted by the context in
which it used, such a phrase is intended to mean any of the listed
elements or features individually or any of the recited elements or
features in combination with any of the other recited elements or
features. For example, the phrases "at least one of A and B;" "one
or more of A and B;" and "A and/or B" are each intended to mean "A
alone, B alone, or A and B together." A similar interpretation is
also intended for lists including three or more items. For example,
the phrases "at least one of A, B, and C;" "one or more of A, B,
and C;" and "A, B, and/or C" are each intended to mean "A alone, B
alone, C alone, A and B together, A and C together, B and C
together, or A and B and C together." Use of the term "based on,"
above and in the claims is intended to mean, "based at least in
part on," such that an unrecited feature or element is also
permissible.
The subject matter described herein can be embodied in systems,
apparatus, methods, computer programs and/or articles depending on
the desired configuration. Any methods or the logic flows depicted
in the accompanying figures and/or described herein do not
necessarily require the particular order shown, or sequential
order, to achieve desirable results. The implementations set forth
in the foregoing description do not represent all implementations
consistent with the subject matter described herein. Instead, they
are merely some examples consistent with aspects related to the
described subject matter. Although a few variations have been
described in detail above, other modifications or additions are
possible. In particular, further features and/or variations can be
provided in addition to those set forth herein. The implementations
described above can be directed to various combinations and sub
combinations of the disclosed features and/or combinations and sub
combinations of further features noted above. Furthermore, above
described advantages are not intended to limit the application of
any issued claims to processes and structures accomplishing any or
all of the advantages.
Additionally, section headings shall not limit or characterize the
invention(s) set out in any claims that may issue from this
disclosure. Further, the description of a technology in the
"Background" is not to be construed as an admission that technology
is prior art to any invention(s) in this disclosure. Neither is the
"Summary" to be considered as a characterization of the
invention(s) set forth in issued claims. Furthermore, any reference
to this disclosure in general or use of the word "invention" in the
singular is not intended to imply any limitation on the scope of
the claims set forth below. Multiple inventions may be set forth
according to the limitations of the multiple claims issuing from
this disclosure, and such claims accordingly define the
invention(s), and their equivalents, that are protected
thereby.
* * * * *